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Autonomous Social Amenity, Pothole Detection and Reporting

Autonomous Social Amenity, Pothole Detection and Reporting

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International Multidisciplinary Information Technology and engineering Conference (IMITEC)

作者： David Mosela Moses Olaifa Pius Owolawi Vusi Malele Dept. Computer Systems Engineering Tshwane University of Technology Tshwane South Africa Dept. of Computer Systems Engineering Tshwane University of Technology Tshwane South Africa

ISBN: (数字)9798350387988

ISBN: (纸本)9798350387995

Challenges faced by traffic management in many developing nations across the globe is a result of the poor quality of road infrastructure. Inadequate and defective road infrastructure are major contributors to this effect. A key element that poses significant risk to road safety and that leads to increased road management cost is the increasing number of potholes and inefficient detection and reporting approach. This study provides an in-dept. exploration of the latest developments in pothole detection systems and examines various methodologies and technologies used in these systems, emphasizing the importance of real-time monitoring for proactive road maintenance. In addition, we proposed a system that addresses the persistent challenge of road infrastructure degradation by adopting cutting-edge technologies, including computer vision, machine learning, and sensor networks (such as ultrasonic and GPIO systems) that are used to identify and assess potholes on road surfaces. To achieve the aim of the study, the system adopts the use of RasberryPi 4 Model B for pothole detection and YOLOv4 for image processing.

关键词： YOLO computer vision Roads Computational modeling Machine learning Hardware Acoustics Road safety Software tools Surface treatment

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Neural Network-Based Analysis of Forest Fire Aftermath in Class-Imbalanced Remote Sensing Earth Image Classification

Neural Network-Based Analysis of Forest Fire Aftermath in Cl...

引用

2024 Symposium on Beyond the Canopy: Technologies and Applications of Remote Sensing

作者： Hnatushenko, Viktoriia Hnatushenko, Volodymyr Soldatenko, Dmytro Dept. Information Technologies and Systems Ukrainian State University of Science and Technologies Dnipro Ukraine Dept. Information Technologies and Computer Engineering Dnipro University of Technology Dnipro Ukraine Institute of Photogrammetry and GeoInformation Leibniz Universität Hannover Germany

Today's agricultural sector is characterized by an important role of accurate mapping and monitoring of agriculture with the help of satellite imagery, which allows to optimize the use of resources, to plan crop areas and to forecast productivity. Classification of satellite images with unbalanced sample distribution is a critical problem in this regard. Traditional machine learning algorithms in particular have limitations in dealing with sample imbalance. In this paper, we proposed convolution neural networks for semantic segmentation, where sample imbalance is considered based on a particular loss function coupled with data augmentation. To illustrate our method, we use Sentinel-2 remote sensing (RS) images covering a number of regions in Ukraine, and then we create an image dataset of the region and for training and testing make data augmentation. The models with different architectural features were investigated. The results demonstrate that the proposed CNN has a higher classification accuracy than the ones discussed in the paper: the classification accuracy on the test dataset reached 96.7% with intersection-over-union values of up to 89.7%. This opens the way for further research in the direction of refining algorithms for classify satellite data with an imbalanced class structure. © Author(s) 2024.

关键词： Semantic Segmentation

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Unconventional Computing based on Four Wave Mixing in Highly Nonlinear Media

Unconventional Computing based on Four Wave Mixing in Highly...

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2023 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2023

作者： Sozos, Kostas Deligiannidis, Stavros Mesaritakis, Charis Bogris, Adonis University of West Attica Dept. of Informatics and Computer Engineering. Aghiou Spiridonos Egaleo Athens12243 Greece University of the Aegean Dept. of Information and Communication Systems Engineering Palama 2 Karlovassi Samos83200 Greece

ISBN: (纸本)9798350345995

Nowadays, photonic analog computing in the form of neuromorphic photonics or photonic neural networks has resulted in powerful hardware platforms that accelerate difficult tasks with marginal power consumption. Although linear transformations are efficiently carried out in the optical domain, nonlinear transformations are also of paramount importance. Here, we propose four wave mixing (FWM) process as an elegant solution for the generation of diverse nonlinear higher order products of an original signal. On one hand, these products can act as activation functions in different types of neural networks, either recurrent neural networks (RNNs) and reservoir computers or in any other type of brain-inspired frameworks. On the other hand, the same products can form the basis for polynomial regression which is a very powerful technique used in machine learning in different tasks i.e. for the prediction of COVID-19 transmission [1]. © 2023 IEEE.

关键词： Recurrent neural networks

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A Multicore Implementation of an AUTOSAR-based XCP Module 12

A Multicore Implementation of an AUTOSAR-based XCP Module

引用

12th Mediterranean Conference on Embedded Computing, MECO 2023

作者： Osama, Mahmoud Hammad, Sherif Elhady, Nancy E. El-Kharashi, M. Watheq Ain Shams University Faculty of Engineering Dept. of Computer & Systems Eng. Cairo Egypt Garraio CEO Cairo Egypt Ain Shams University Faculty of Engineering Dept. of Mechatronics Eng. Cairo Egypt

ISBN: (纸本)9798350322910

The automotive industry depends on computers to control and monitor vehicles behaviour. The Universal Measurement and Calibration Protocol (XCP) connects calibration systems to electronic control units (ECUs). Nowadays, AUTOSAR-based XCP for single core ECUs is widely used in automotive industry. As the demand for computing power is rapidly increasing in the automotive domain, car manufacturers are gradually introducing multi-core ECUs in their electronic architectures. Multi-core ECUs provide high level of parallelism. This research work aims to deliver a multi-core implementation of an AUTOSAR-based XCP module, as it is important to have an efficient process of measurement and calibration on multi-core targets. The paper results are ensuring data consistency on the application level, avoiding race conditions between writer core and reader core which might result in processing rubbish data, and decreasing data dependency between cores. © 2023 IEEE.

关键词： Automotive industry

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Stealthy Attack Detection of controlled ramp meters in freeway networks 63

Stealthy Attack Detection of controlled ramp meters in freew...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Menelaou, Charalambos Zhang, Kangkang Timotheou, Stelios Panayiotou, Christos G. Parisini, Thomas University of Cyprus Kios CoE Dept. of Electrical and Computer Engineering Cyprus Imperial College London Dept. of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom Aalborg University Department of Electronic Systems Denmark University of Trieste Department of Engineering and Architecture Italy

ISBN: (纸本)9798350316339

The increasing reliance on intelligent transportation systems (ITS) for traffic management has simultaneously heightened the potential for cybersecurity threats. Malicious cyber attacks on such systems can lead to operational inefficiencies, heightened congestion, and compromised safety. This work introduces a stealthy integrity attack tailored for ramp metered freeway systems. The attack model is conceptualized as a closed-loop dynamical system, formulated as an optimization problem based on the Cell Transmission Model. This work further proposes a distributed detection mechanism designed to identify attack residuals, which, by incorporating an adaptive threshold scheme, can detect the presence of an attack. To demonstrate the efficacy of our detection scheme, we present a scenario illustrating its application in freeway road networks. © 2024 IEEE.

关键词： Cyber attacks

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Enhancing Connectivity in Remote Regions using Adaptive and Resilient UAV-Based Wireless Networks

Enhancing Connectivity in Remote Regions using Adaptive and ...

引用

IEEE International Workshop on computer-Aided Modeling, Analysis, and Design of Communication Links and Networks, CAMAD

作者： Andrés García–Lopez Diego Ramos–Ramos Santiago García–Gil José Gómez–delaHiz Juan Manuel Murillo Jaime Galán–Jiménez Dept. of Computer Systems and Telematics Engineering University of Extremadura Spain

ISBN: (数字)9798350377644

ISBN: (纸本)9798350377651

The annual growth of Internet adoption still leaves around 2 billion people in remote areas without connectivity, cutting them off from vital services such as healthcare and distance education. This paper proposes an energy-efficient solution leveraging Unmanned Aerial Vehicles (UAVs) based network architectures to dynamically adapt to hardware failures or battery depletion. By incorporating the Software-Defined Networking (SDN) paradigm for real-time network management, this approach ensures swift adaptability to changing environmental conditions. Consequently, the UAV-based network can deliver Internet of Things (IoT) applications to users, significantly improving the quality of life for rural populations. Simulations conducted in realistic scenarios demonstrate a success rate of over 90% in handling user requests across various situations.

关键词： Adaptation models Wireless networks Autonomous aerial vehicles Energy efficiency Real-time systems Hardware Batteries Internet of Things Vehicle dynamics Software defined networking

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A Deep Averaged Reinforcement Learning Approach for the Traveling Salesman Problem

A Deep Averaged Reinforcement Learning Approach for the Trav...

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2022 IEEE International Conference on systems, Man, and Cybernetics, SMC 2022

作者： Parasteh, Sirvan Khorram, Amin Mouhoub, Malek Sadaoui, Samira University of Regina Dept. of Computer Science Regina Canada University of Regina Dept. of Industrial Systems Engineering Regina Canada

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

This work presents a deep averaged reinforcement-learning approach to learn improvement heuristics for route planning. The proposed method is tested on the Traveling Salesman Problem (TSP). While learning improvement heuristics using machine learning models are prosperous, these methods suffer from low generalization and forgetfulness of the agents during the training process. We have applied the stochastic weight averaging method during the training phase to solve these issues, which smothers the training convergence and prevents the forgetting of optimized learned policies, and consequently provides better results. The agent can learn the optimized policy while holding a moving average of the previously learned policies during the training epochs. In order to assess the performance of our proposed approach, we conducted comparative experiments considering other known methods from the literature. The results demonstrate our proposed method's superiority in training trends and optimization. © 2022 IEEE.

关键词： Reinforcement learning

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A Comprehensive Threat Model for Autonomous Vehicle Communication Security 6

A Comprehensive Threat Model for Autonomous Vehicle Communic...

引用

6th International Conference on Advancements in Computing, ICAC 2024

作者： Meegammana, Niranjan W. Fernando, Harinda Shilpa Sayura Foundation Cyber Security Research Lab Kandy Sri Lanka Sri Lanka Institute of Information Technology Dept. of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798331517878

The rapid advancement of autonomous vehicles (AVs) promises to enhance transportation efficiency, reliability, and safety. However, these systems, particularly multi-channel vehicular communications, are vulnerable to cyber attacks that threaten authenticity, authorization, confidentiality, integrity, availability, and non-repudiation. The 2014 Chrysler Jeep hack highlights the urgent need for robust security measures in vehic-ular communications. This study utilizes the STRIDE, DREAD, and MITRE ATT&CK security frameworks to develop a comprehensive threat model that identifies and assesses critical threats and risks to AV communications. By detailing specific attack vectors and their multidimensional impacts and criticality, the study establishes a baseline of threats for communication security in vehicular networks. It produces visual threat maps, impact graphs of various attacks, and a color-coded threat grid to guide stakeholders in developing effective cyber security measures for protecting AV communications, thereby help stakeholders to improve the reliability and safety of AV navigation for fostering growth in the AV industry. © 2024 IEEE.

关键词： Cyber attacks

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Online Canonical Correlation Analysis via Rayleigh-Ritz Projections 58

Online Canonical Correlation Analysis via Rayleigh-Ritz Proj...

引用

58th Asilomar Conference on Signals, systems and computers, ACSSC 2024

作者： Kalantzis, Vassilis Traganitis, Panagiotis A. Kanatsoulis, Charilaos I. Ibm Research MIT-IBM Watson Ai Lab Yorktown HeightsNY United States Michigan State University Dept. of Electrical and Computer Engineering East LansingMI United States University of Pennsylvania Dept. of Electrical and Systems Engineering PhiladelphiaPA United States

ISBN: (纸本)9798350354058

Canonical correlation analysis (CCA) is a classical statistical tool that enables processing of multiview data in a plethora signal processing, machine learning and data mining applications, by identifying a common linear subspace from the available data views. Most algorithms tackling the CCA task require all the data per view to be available. Nevertheless, in many cases, data are not available in batch form and may arrive in streaming fashion. This work puts forth a novel, computationally efficient, projection based method for identifying and updating the common subspace on-the-fly, as new data arrive, while retaining its' fidelity. Preliminary numerical tests, on synthetic and real data benchmarks, showcase the potential of the proposed method. © 2024 IEEE.

关键词： Canonical Correlation Analysis Online Analy-sis Rayleigh-Ritz Projection

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Arabic Sentiment Analysis of Mobile Banking Services Reviews 10

Arabic Sentiment Analysis of Mobile Banking Services Reviews

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10th International Conference on Social Networks Analysis, Management and Security, SNAMS 2023

作者： Khabour, Safaa M. Mustafa, Dheya Althebyan, Qutaibah Yarmouk University Faculty of Information Technology and Computer Sciences Dept. of Information Systems Irbid21163 Jordan The Hashemite University Faculty of Engineering Dept. of Computer Engineering Zarqa13133 Jordan Al Ain University Dept. of Cybersecurity Al Ain United Arab Emirates Jordan University of Science and Technology Irbid Jordan

ISBN: (纸本)9798350318906

Online customer reviews have developed into a significant source of information about a business's performance. Due to shifting consumer expectations and growing internet penetration, the Middle East, especially Jordan, is seeing an increase in the usage of online banking. This propensity toward digital transition has been accelerated by the COVID-19 pandemic. Mobile banking service companies aim to effectively use customers' feedback to identify areas for improvement in customer satisfaction. Although Arabic is becoming one of the most widely used languages on the Internet, only a few studies have focused on Arabic sentiment analysis to date. The present study conducts an extensive emotion mining and sentiment analysis on mobile banking reviews in Arabic, exploiting machine learning, natural language processing, and resampling methods to obtain subjective feedback, determine polarity, and identify customers' feelings in the banking domain. This work walks the reader through detailed steps of cleaning and preprocessing a manually annotated dataset of online banking reviews in Arabic, followed by features extraction and modeling, dataset splitting, and balancing training data. Finally, we examined classification algorithms including Decision Tree (DT), Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), K-Nearest Neighbors (KNN), and Random Forest (RF). Ensemble hard and soft voting ML methods were also evaluated. We achieved a maximum accuracy of about 94% using the LR classifier and a maximum precision of 94% using the LR and hard voting methods. Our methodology will enable banks to acquire insights on how to develop their online presence and meet the demands of customers and stakeholders. © 2023 IEEE.

关键词： Sentiment analysis

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